A model has been developed to explore the possibility of creating self-contained micro-grids using a combination of renewable energy and energy storage. The model, written in Python, uses solar and wind availability over the period of interest to estimate the amount of available renewable energy. It then compares the renewable energy generated to a given electric load profile over the same period to determine when to utilize on-site energy storage. The renewable energy calculations are performed using the NREL System Advisor Model for both solar and wind resources. Using a vector analysis, the model calculates the time periods the system could charge, or discharge energy based on the amount of additional renewable energy that is not directly utilized to the demand profile. The model includes the ability to utilize several different types of storage including lithium-ion batteries, vanadium flow batteries, pumped thermal energy storage (PTES) or any combination of storage technologies. Additional resources have been modeled including an on-site gas engine and a local utility connection. The power generation calculations can then be used to estimate the capital costs, operating costs and the levelized cost of electricity (LCOE) for the microgrid. A case study has been completed examining the Southwest Research Institute (SwRI) campus using their unique economic factors over a 30- year period. The case study shows that the SwRI would only be able to provide up to 43% of their total load using renewable energy alone while decreasing the cost of electricity by around 10%. Increasing renewable penetration and adding storage showed that the system could provide around 50% of the required load without increasing electricity costs and that the SwRI campus could provide up to 60% of its demand with an increase in the LCOE.
